Neuromorphic Computing Architectures for Ultra-Low Latency Edge Intelligence Applications
Keywords:
neuromorphic computing, edge intelligence, ultra-low latency, spiking neural networks, event-driven architecture, hardware-software co-design, energy efficiency, socio-technical infrastructure, robustness, fairnessAbstract
The proliferation of real-time edge intelligence applications, from autonomous vehicles to industrial control systems, demands computing substrates that offer unprecedented reductions in latency while operating under severe energy constraints. Neuromorphic computing, inspired by the biological principles of neural information processing, has emerged as a promising architectural paradigm to address these requirements. This paper presents a comprehensive analysis of neuromorphic computing architectures designed specifically for ultra-low latency edge intelligence. We examine the foundational principles of event-driven, spiking neural network-based computation and contrast them with conventional von Neumann and GPU-accelerated systems. The analysis spans multiple levels of abstraction, including circuit-level design choices, network-on-chip topologies, and system-level integration with edge infrastructure. Key structural trade-offs are explored, such as the balance between spike-time encoding precision and energy efficiency, the implications of asynchronous versus synchronous communication, and the scalability of local learning rules. Deployment considerations are discussed in the context of heterogeneous edge environments, addressing issues of reliability, thermal management, and interoperability with existing cloud-fog hierarchies. Beyond technical performance, the paper critically evaluates governance, robustness, and fairness dimensions, arguing that neuromorphic architectures introduce novel vulnerabilities and access asymmetries that require proactive policy frameworks. Through cross-domain comparisons with digital accelerators, analog in-memory computing, and quantum alternatives, we delineate the regimes in which neuromorphic approaches offer unique advantages. The paper concludes with a forward-looking assessment of sustainability, standardization needs, and the role of neuromorphic computing in shaping resilient, equitable edge intelligence ecosystems. Our synthesis provides a system-level roadmap for researchers, engineers, and policymakers navigating the transition to biologically inspired computing at the network periphery.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
